An optimal error nonlinearity for robust adaptation against impulsive noise
Signal Processing Advances in Wireless Communications(2013)
摘要
The least-mean squares algorithm is non-robust against impulsive noise. Incorporating an error nonlinearity into the update equation is one useful way to mitigate the effects of impulsive noise. This work develops an adaptive structure that parametrically estimates the optimal error-nonlinearity jointly with the parameter of interest, thus obviating the need for a priori knowledge of the noise probability density function. The superior performance of the algorithm is established both analytically and experimentally.
更多查看译文
关键词
adaptive estimation,filtering theory,impulse noise,least mean squares methods,probability,LMS filter,adaptive structure,error nonlinearity,impulsive noise,least-mean squares algorithm,noise probability density function,optimal error nonlinearity,optimal error-nonlinearity,robust adaptation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要